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Integrating genetic algorithm with AnnAGNPS for optimizing BMPs placement to reduce sheet/rill and ephemeral gully erosion

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SOIL & TILLAGE RESEARCH 2025年 252卷

作者： Mohebzadeh, Hamid Biswas, Asim DeVries, Ben Rudra, Ramesh Yang, Wanhong Daggupati, Prasad Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Univ Guelph Sch Environm Sci Guelph ON N1G 2W1 Canada Univ Guelph Dept Geog Environm & Geomat Guelph ON Canada

In order to effectively reduce nonpoint source pollutants in agricultural areas within a watershed, a combination of Best Management Practices (BMPs) is selected based on their economic and environmental effectiveness. However, determining the optimal combination can be challenging due to the implementation costs and the consideration of decision makers' preferences. This research presents a methodology for integrating a genetic algorithm with the Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to effectively select the most efficient BMPs placement for a given watershed. By optimizing BMPs placement, the model can minimize sediment loads from different types of erosion, including sheet/rill, ephemeral gully, and total erosion at the minimal cost. Results demonstrated that BMP placement by the optimization model reduced sediment load caused by sheet/rill by 84.6 %, ephemeral gully by 85.4 %, and total erosion by 86.3 % in the study watershed. Additionally, the model achieved these results at a minimal cost, making it a cost-effective solution for sediment load reduction in the watershed. Also, the results showed the effective implementation of the developed optimization approach for strategically locating BMPs in specific areas, rather than implementing them throughout the entire watershed. By targeting these areas and implementing suitable BMPs, the model was able to reduce the amount of sediment load and remain cost-effective. The proposed weighted overlay technique helped to place BMPs within agricultural fields instead of AnnAGNPS cells, making it easier for farmers to adopt and effectively reduce sediment load in each field. The developed model in the current study can be applied by decision makers in other watersheds with limited resources for implementing BMPs.

关键词： AnnAGNPS genetic algorithm optimization Soil erosion Best management practices

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A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data

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JOURNAL OF SOILS AND SEDIMENTS 2024年第7期24卷 2816-2828页

作者： Wang, Jiawei Wu, Yongyi Zhang, Yulu Wang, Honghao Yan, Hong Jin, Hua Taiyuan Univ Technol Coll Water Conservancy Sci & Engn Taiyuan 030024 Peoples R China Taiyuan Bilan Water Conservancy Engn Design Co Ltd Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Mat Sci & Engn Taiyuan 030024 Peoples R China

Purpose Accurate assessment of soil moisture content (SMC) is crucial for applications in climate science, hydrology, ecology, and agriculture. However, conventional SMC characterization and measurement are expensive, time-consuming, and have negative effects on soil. Recently, the application of multispectral technology provides a new idea for SMC accurate detection. The objective of this study was to develop and compare regression and machine learning algorithms to estimate SMC from multispectral images. Materials and methods A multispectral sensor was used to collect spectral images of 125 soil samples from five distinct soil textures in Shanxi province at varying degrees of soil moisture, ranging from arid to fully saturated. A set of seven spectral parameters was derived from images, and predictive relationships were developed against laboratory-measured SMC. A linear regression (LR) model and a backpropagation neural network model based on genetic algorithm optimization (GA-BP) were compared in this study to predict SMC. Results and discussion The results showed that (1) the spectral reflectance and SMC exhibit a clear negative correlation, and the lower the SMC, the larger the spectral reflectance is. (2) The GA-BP neural network model exhibits higher prediction accuracy and performance (R-2 = 0.978 similar to 0.990, RMSE = 0.366 similar to 0.799%, MAE = 0.360 similar to 0.890%). (3) The GA-BP model exhibits the excellent inversion precision for the fine sand soil (R-2 = 0.990, RMSE = 0.518%, MAE = 0.360%). Conclusions This study introduces an effective methodology for accurate estimation of SMC using multispectral remote sensing technology. It further underscores the significant effectiveness of employing backpropagation neural networks and genetic algorithms in SMC prediction, providing a rapid, precise, non-intrusive, and practical approach towards precision agriculture.

关键词： Soil moisture content Spectral characteristics Neural network model genetic algorithm optimization

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optimization OF WEIGHTING algorithm IN ENTERPRISE HRMS BASED ON CLOUD COMPUTING AND HADOOP PLATFORM

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3970-3978页

作者： Zhao, Genliang Anhui Business Coll Sch Int Business & Tourism Wuhu 241002 Peoples R China

As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both genetic algorithm (GA) and Grey Wolf optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost alpha, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function zeta , incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings.

关键词： Cloud based-HRMS genetic algorithm optimization hadoop platform load balancing processing unit allocation cost function optimization

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An Early Warning Model for Student Status Based on genetic algorithm-Optimized Radial Basis Kernel Support Vector Machine

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第2期20卷 263-272页

作者： Li, Hui Huang, Qixuan Wang, Chao Handan Univ Sch Informat Engn Handan Peoples R China Hebei key Lab Opt Fiber Biosensing & Commun Device Handan Peoples R China Handan Univ Sch Software Handan Peoples R China

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

关键词： Early Warning Model genetic algorithm optimization Radial Basis Kernel Support Vector Machine

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optimization-Based Mitigation Techniques of the Temporary Overvoltage in Large Offshore Wind Farm

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IEEE ACCESS 2023年 11卷 6320-6330页

作者： Said, Abdelrahman Ezzat, Mahmoud Abd-Allah, Mousa A. A. Fouda, Mostafa M. M. Abouelatta, Mohamed A. A. Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo 11672 Egypt Idaho State Univ Coll Sci & Engn Dept Elect & Comp Engn Pocatello ID 83209 USA

Overvoltage transient is the major danger facing large offshore wind farms, as it may lead to a complete failure of the system components. A 150 MW offshore wind farm was modeled using the ATP-EMTP package to analyze the effect of the temporary overvoltage resulting from de-energization conditions, and the ferro resonance phenomenon resulting from the asymmetric opening of circuit breaker pole(s). The results showed that the best scenario to shut down such a system from the grid with minimum values of temporary overvoltage is to disconnect it, feeder, by a feeder. The stuck of one or more poles during the opening operation of the circuit breaker is a dangerous phenomenon, as it induces a high overvoltage value, which lasts for a long time causing more electric stress on the sensitive components of the system. The use of the Pre-Insertion Resistor PIR was recommended as a mitigation technique for the temporary overvoltage in large offshore wind farms, as it proves a significant reduction in the overvoltage values reaches to 79.44 % compared to surge arrester and shunt reactor which reduce the over voltage by 68.33%, 66.29% respectively. The genetic algorithm was used to find the optimum value of the PIR and shunt reactor that leads to the minimum TOV. The optimum value of PIR which was recorded is equal to 57.352 Ohm, which leads to more reduction in the overvoltage value by 9.67%. Also, the optimum values of the resistor and inductor of the shunt reactor were found to be 11.238 Ohms and 37.246 mH which leads to more reduction in the overvoltage value by 21.97% compared to the reduction without optimization.

关键词： Voltage control Wind turbines Transient analysis Wind farms Circuit faults Circuit breakers Rotors Temporary overvoltage overvoltage mitigation ATP modeling genetic algorithm optimization

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optimization of cellulose extraction process from sugar beet pulp and preparation of its nanofibers with choline chloride-lactic acid deep eutectic solvents

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BIOMASS CONVERSION AND BIOREFINERY 2023年第16期13卷 14457-14469页

作者： Soleimanzadeh, Hamid Bektashi, Fatemeh Mirzaee Ahari, Samaneh Zamani Salari, Dariush Olad, Ali Ostadrahimi, Alireza Univ Tabriz Fac Chem Dept Appl Chem Tabriz Iran Tabriz Univ Med Sci Fac Nutr & Food Sci Nutr Res Ctr Tabriz Iran

In this novel bench-top experimental and statistical investigation, the effects of influencing parameters on cellulose extraction have been studied and optimized. Chemically isolated cellulose was used at the optimum condition for the purpose of cellulose nanofiber preparation by deep eutectic solvent (DES) application. For this purpose, the Taguchi method was applied for experimental design. Six parameters which have more signal-to-noise ratio were selected as influencing parameters. These parameters consist of solvent ratio for dewaxing, depectinization pH and time, delignification pH and time, and pH of hemicellulose removal. The effect of these parameters on the cellulose extraction was modeled and optimized by the application of artificial neural network (ANN), coupled with genetic algorithm (GA). The ANN model, by establishing of feedforward backpropagation network with topologies 6, 13, and 1, was applied to model the relation between the extraction condition and cellulose extraction. The results confirmed that the best neural network structure has the ability of cellulose extraction yield prediction with an acceptable level of accuracy. The optimization results show the maximum cellulose extraction would be achieved at the following conditions: pectin extraction time of 1.62 h, delignification time of 2.19 h, the solvent ratio for dewaxing of 0.89 (V/V%), depectinization pH of 2.06, delignification pH of 5.48, and hemicellulose removal pH of 11.36. Under these conditions, the experimental extraction condition yield was 91.7% for cellulose extraction, which matched with the predictive extraction yield of 92.3%. Chemically extracted cellulose at the above condition was applied to prepare cellulose nanofibers. For this purpose, choline chloride-lactic acid-based DES pretreatment was used. The structure of the extracted cellulose and prepared cellulose nanofibers by the application of FTIR, XRD, Fe-SEM, and TEM methods was characterized. The XRD diffractograms a

关键词： Cellulose extraction optimization Artificial neural network genetic algorithm optimization Cellulose nanofibers Deep eutectic solvent (DESs)

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Co-optimization of operating parameters, fuel composition, and piston bowl geometry of gasoline compression ignition (GCI) engine at high loads

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2023年第9期24卷 4044-4062页

作者： Zhang, Pengwei Duan, Huiquan Xu, Guangfu Li, Yaopeng Jia, Ming Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China

Focusing on the gasoline compression ignition (GCI) combustion mode, the operating parameters, fuel composition, and piston bowl geometry were collaboratively optimized at high loads to improve engine performance. A meliorative genetic algorithm coupled with a three-dimensional computational fluid dynamics (CFD) program was adopted as the optimization tool. The optimal GCI combustion strategy is summarized based on the optimization results, and the key parameters affecting engine performance at high loads are further analyzed. The results show that the employment of the open-type piston bowl, high-reactivity fuel, and late start of injection (SOI) is desirable for GCI engines at high loads. Under this strategy, fuel burns in multiple stages, which can reduce the heat release rate and advance the combustion phase. The width of the piston bowl is the geometric parameter with the most notable impact on GCI combustion. Due to the weaker turbulent kinetic energy and the smaller surface area, the larger piston bowl width is conducive to diminishing the heat transfer losses of the engine and promoting the oxidation of unburned hydrocarbon (HC) emissions. Relative to the optimal case, the strategy with the open-type piston bowl, high-reactivity fuel, and early SOI can reduce fuel consumption but results in a higher in-cylinder pressure rise rate and increased ringing intensity (RI). On the contrary, the re-entrant type piston bowl, low-reactivity fuel, and late SOI can significantly reduce the pressure rise rate and RI while deteriorating thermal efficiency.

关键词： Gasoline compression ignition (GCI) genetic algorithm optimization collaborative optimization fuel composition piston bowl geometry

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Thermodynamic assessment of the dry-cooling supercritical Brayton cycle in a direct-heated solar power tower plant enabled by CO2-propane mixture

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RENEWABLE ENERGY 2023年第1期203卷 649-663页

作者： Ma, Ning Meng, Fugui Hong, Wenpeng Li, Haoran Niu, Xiaojuan Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China

This paper proposes an improved recompression combined cycle configuration to explore the potential of using CO2-propane in a direct-heating solar power tower (SPT) plant. The effects of critical parameters on overall performance were detailly analyzed. The exergy distribution of CO2 and CO2-propane cycles was compared respectively after being optimized by a genetic algorithm. The results show that the exergy loss of regenerators was reduced, and the entire exergy efficiency of the SPT plant was improved by configuring two high -temperature regenerators. The cycle performance of propane, serving as the second additive, becomes more significant when the cooling temperature is increased. The overall thermal efficiency of the CO2-propane-configured SPT plant is 2.08% higher than using CO2 alone at a condensation temperature of 50 degrees C. Meanwhile, the exergy efficiency of the SPT plant with CO2-propane cycle decreases less (1.67%) than CO2 (2.02%) when the condensation temperature rises from 42 degrees C to 50 degrees C. However, the temperature difference of the receiver would decrease when using CO2-propane. This study provides a valuable reference for the application of CO2-propane mixture in the dry-cooling Brayton cycle of the direct-heated SPT plant.

关键词： Supercritical Brayton cycle Carbon dioxide Binary mixture Solar power tower plant Thermodynamic analysis genetic algorithm optimization

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A Waveguide Slot-Fed Patch Antenna Array

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2023年第8期22卷 2027-2031页

作者： Hoseini, Mehri Foororaghi, Keyvan Abdolali, Ali Tarbiat Modares Univ Dept Elect & Comp Engn Tehran *** Iran Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

This letter proposes an optimal patch based on the Haar function series and a genetic algorithm to design waveguide-fed traveling-wave arrays. A supercell is designed that provides better pattern characteristics. The conductance and susceptance equations are obtained in terms of offset and resonance length from simulation results. Using Elliot's design equations of the proposed structure, a waveguide slot-fed traveling-wave path antenna array is designed and fabricated for broadside radiation at 9.5 GHz frequency. The gain of the proposed array is 17.6 and 17.7 dB, and the side lobe level is -22.5 and -21 dB in the H-plane and E-plane, respectively. Near broadside radiation is achieved with good radiation efficiency in the high-frequency range.

关键词： Elliot's design equations genetic algorithm optimization Haar function series traveling-wave array waveguide-fed patch antenna array

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optimization of Savonius turbine clusters using an evolutionary based genetic algorithm 10

Optimization of Savonius turbine clusters using an evolution...

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10th International Conference on Applied Energy (ICAE)

作者： Zheng, Y. Bai, H. L. Chan, C. M. Hong Kong Univ Sci & Technol Kowloon Dept Civil & Environm Engn Clear Water Bay Hong Kong Peoples R China

VAWTs benefit from constructive coupling effect that significantly improves efficiency. This work aims at optimizing the configuration of turbine clusters with three Savonius turbines. To maximize the aggregate power output of a turbine cluster, two-dimensional (2D) computational fluid dynamics (CFD) is utilized, along with an evolutionary based genetic algorithm (GA). Four scenarios are investigated considering different combinations of rotational directions. For the optimal cluster obtained, a highest 37% increase in averaged power coefficient ((C) over bar (p))over an isolated Savonius turbine is achieved at Tip Speed Ratio (TSR) = 0.8. The substantial increase in efficiency for turbines in the optimal cluster is essentially due to constructive coupling in the flow field. Two major effective coupling mechanisms are proposed based on flow structures to explain the observed performance increase. Furthermore, a method to expand the optimal cluster into compact wind farms is proposed. It is verified that for a compact Savonis-type VAWT wind farm that consists of 12 turbines, a 30% increase in (C) over barp is obtained, showing the practicality of constructing efficient VAWT wind farms in areas with limited land. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Wind energy Vertical Axis Wind Turbines Savonius turbine genetic algorithm optimization

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